






"""simple docstring"""


import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
		import torch




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Any      =  """char"""
					lowercase_    :				Union[str, Any]      =  """bpe"""
					lowercase_    :				Optional[Any]      =  """wp"""


__SCREAMING_SNAKE_CASE   =   (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				List[Any]      =  ["""image_processor""", """char_tokenizer"""]
					lowercase_    :				int      =  """ViTImageProcessor"""
					lowercase_    :				List[str]      =  """MgpstrTokenizer"""
					def __init__(							self							:	int     , UpperCAmelCase							:	Tuple=None     , UpperCAmelCase							:	List[Any]=None     , **UpperCAmelCase							:	List[Any]   )	->       List[Any]:
											lowerCAmelCase   :str						=			None
											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																	    ' instead.'     , UpperCAmelCase     , )
																	lowerCAmelCase   :List[str]						=			kwargs.pop('feature_extractor'   )

											lowerCAmelCase   :Union[str, Any]						=			image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																	raise ValueError('You need to specify an `image_processor`.'   )
											if tokenizer is None:
																	raise ValueError('You need to specify a `tokenizer`.'   )

											lowerCAmelCase   :Tuple						=			tokenizer
											lowerCAmelCase   :str						=			AutoTokenizer.from_pretrained('gpt2'   )
											lowerCAmelCase   :Tuple						=			AutoTokenizer.from_pretrained('bert-base-uncased'   )

											super().__init__(UpperCAmelCase     , UpperCAmelCase   )
					def __call__(							self							:	Tuple     , UpperCAmelCase							:	int=None     , UpperCAmelCase							:	List[Any]=None     , UpperCAmelCase							:	Tuple=None     , **UpperCAmelCase							:	int   )	->       Union[str, Any]:
											if images is None and text is None:
																	raise ValueError('You need to specify either an `images` or `text` input to process.'   )

											if images is not None:
																	lowerCAmelCase   :Optional[int]						=			self.image_processor(UpperCAmelCase     , return_tensors=UpperCAmelCase     , **UpperCAmelCase   )
											if text is not None:
																	lowerCAmelCase   :Union[str, Any]						=			self.char_tokenizer(UpperCAmelCase     , return_tensors=UpperCAmelCase     , **UpperCAmelCase   )

											if text is None:
																	return inputs
											elif images is None:
																	return encodings
											else:
																	lowerCAmelCase   :Optional[Any]						=			encodings['input_ids']
																	return inputs
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Optional[int]   )	->       int:
											lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			sequences
											lowerCAmelCase   :List[str]						=			char_preds.size(0   )

											lowerCAmelCase	, lowerCAmelCase   :List[str]						=			self._decode_helper(UpperCAmelCase     , 'char'   )
											lowerCAmelCase	, lowerCAmelCase   :List[str]						=			self._decode_helper(UpperCAmelCase     , 'bpe'   )
											lowerCAmelCase	, lowerCAmelCase   :int						=			self._decode_helper(UpperCAmelCase     , 'wp'   )

											lowerCAmelCase   :Union[str, Any]						=			[]
											lowerCAmelCase   :List[Any]						=			[]
											for i in range(UpperCAmelCase   ):
																	lowerCAmelCase   :Optional[Any]						=			[char_scores[i], bpe_scores[i], wp_scores[i]]
																	lowerCAmelCase   :Optional[Any]						=			[char_strs[i], bpe_strs[i], wp_strs[i]]
																	lowerCAmelCase   :Dict						=			scores.index(max(UpperCAmelCase   )   )
																	final_strs.append(strs[max_score_index]   )
																	final_scores.append(scores[max_score_index]   )

											lowerCAmelCase   :int						=			{}
											lowerCAmelCase   :Tuple						=			final_strs
											lowerCAmelCase   :Any						=			final_scores
											lowerCAmelCase   :Any						=			char_strs
											lowerCAmelCase   :Tuple						=			bpe_strs
											lowerCAmelCase   :Union[str, Any]						=			wp_strs
											return out
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	int   )	->       List[Any]:
											if format == DecodeType.CHARACTER:
																	lowerCAmelCase   :Union[str, Any]						=			self.char_decode
																	lowerCAmelCase   :int						=			1
																	lowerCAmelCase   :Any						=			'[s]'
											elif format == DecodeType.BPE:
																	lowerCAmelCase   :Optional[Any]						=			self.bpe_decode
																	lowerCAmelCase   :List[str]						=			2
																	lowerCAmelCase   :List[Any]						=			'#'
											elif format == DecodeType.WORDPIECE:
																	lowerCAmelCase   :Dict						=			self.wp_decode
																	lowerCAmelCase   :List[str]						=			102
																	lowerCAmelCase   :List[Any]						=			'[SEP]'
											else:
																	raise ValueError(f"""Format {format} is not supported."""   )

											lowerCAmelCase	, lowerCAmelCase   :List[str]						=			[], []
											lowerCAmelCase   :Union[str, Any]						=			pred_logits.size(0   )
											lowerCAmelCase   :str						=			pred_logits.size(1   )
											lowerCAmelCase	, lowerCAmelCase   :int						=			pred_logits.topk(1     , dim=-1     , largest=UpperCAmelCase     , sorted=UpperCAmelCase   )
											lowerCAmelCase   :List[str]						=			preds_index.view(-1     , UpperCAmelCase   )[:, 1:]
											lowerCAmelCase   :List[str]						=			decoder(UpperCAmelCase   )
											lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			torch.nn.functional.softmax(UpperCAmelCase     , dim=2   ).max(dim=2   )
											lowerCAmelCase   :List[str]						=			preds_max_prob[:, 1:]

											for index in range(UpperCAmelCase   ):
																	lowerCAmelCase   :Union[str, Any]						=			preds_str[index].find(UpperCAmelCase   )
																	lowerCAmelCase   :Union[str, Any]						=			preds_str[index][:pred_eos]
																	lowerCAmelCase   :Tuple						=			preds_index[index].cpu().tolist()
																	lowerCAmelCase   :Union[str, Any]						=			pred_index.index(UpperCAmelCase   ) if eos_token in pred_index else -1
																	lowerCAmelCase   :str						=			preds_max_prob[index][: pred_eos_index + 1]
																	lowerCAmelCase   :Dict						=			pred_max_prob.cumprod(dim=0   )[-1] if pred_max_prob.nelement() != 0 else 0.0
																	dec_strs.append(UpperCAmelCase   )
																	conf_scores.append(UpperCAmelCase   )

											return dec_strs, conf_scores
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Union[str, Any]   )	->       Optional[Any]:
											lowerCAmelCase   :List[str]						=			[seq.replace(' '     , ''   ) for seq in self.char_tokenizer.batch_decode(UpperCAmelCase   )]
											return decode_strs
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	List[Any]   )	->       Any:
											return self.bpe_tokenizer.batch_decode(UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Optional[int]   )	->       int:
											lowerCAmelCase   :int						=			[seq.replace(' '     , ''   ) for seq in self.wp_tokenizer.batch_decode(UpperCAmelCase   )]
											return decode_strs







"""simple docstring"""


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE   =   {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   'hopper-medium-v2'
		__SCREAMING_SNAKE_CASE   =   gym.make(env_name)

		__SCREAMING_SNAKE_CASE   =   ValueGuidedRLPipeline.from_pretrained(
		    'bglick13/hopper-medium-v2-value-function-hor32',
		    env=env,
		)

		env.seed(0)
		__SCREAMING_SNAKE_CASE   =   env.reset()
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   10_00
		__SCREAMING_SNAKE_CASE   =   [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						__SCREAMING_SNAKE_CASE   =   pipeline(obs, planning_horizon=32)

						# execute action in environment
						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   env.step(denorm_actions)
						__SCREAMING_SNAKE_CASE   =   env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
						    F""" {total_score}"""
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						__SCREAMING_SNAKE_CASE   =   next_observation
		except KeyboardInterrupt:
				pass

		print(F"""Total reward: {total_reward}""")